 Hi, I'm Brianna Frank. I am the director of product at IBM Cloud and I'm so excited to be here at Kupon 2020. So today I'm gonna talk about this concept of Kubernetes but needed everywhere. And I'm gonna walk you through a few real world examples that I'm seeing across a couple different industries where Kubernetes is needed, but it's needed in lots of different locations. So the first example is in the financial industry and on the surface the financial industry might seem like it's all about money but it's really all about technology. And in order to compete financial institutions need to create exceptional user experiences and they leverage AI and trading algorithms to help stay ahead of the market. But it is also a very heavily regulated industry. And so financial services clients need to iterate quickly leveraging cloud native tools and best practices but they need to be able to keep that data secure and compliant. And sometimes that means they might have, there might be some restrictions on where that data can live. So here's a scenario that's emerging to solve that problem distributed cloud or Kubernetes as a service anywhere. And Gardner refers to it as distributed cloud, IDC calls it local cloud as a service but this is a concept where the public cloud is now extended to lots of different locations, different public cloud examples, on-prem examples or even at the edge. And so the key point here is that distributed cloud enables you to take advantage of a public cloud consumption model but in many different locations. And you don't have to be an expert in running these software stacks to run them outside of the public cloud. So let's take a look at an example of this concept of Kubernetes everywhere in action. And so in this example, one of our clients is really focused on worker safety. And so they're leveraging distributed analytics to keep workers safe. So in this scenario, you might have a business or an office building that is actually under construction and you need to be able to leverage video analytics to tell if someone is wearing a hard hat or not and warn them before they move into a potentially dangerous area of that building. So in this scenario, you want to use video analytics but you really can't have any latency because if you warn someone too late they could possibly be injured or hurt. So let's take a look at how this scenario might works. So if you think about solving a safety problem you need to be able to analyze video data from many cameras all over the office building and it's impractical to send all of that data back to the cloud to be processed remotely. So for this scenario to work, well, it would be better the video processing were to happen closer to the device. So traditionally to do that, it would require you to install servers and software and manage them on-prem. With Kubernetes and distributed cloud, we can extend the public cloud into the office so we can leverage cloud services to run this application close to the device. And that's critical. So we can reduce latency and we can effectively warn someone if they enter a dangerous area. So now what's interesting is that same worker scenario and client is having to evolve. And because of COVID there are new demands and new challenges that we have to adapt to. And so now that same client instead of having to warn someone about a hard hat we're really worried about making sure folks are wearing masks and making sure folks are wearing masks correctly and that the mask hasn't slipped down under the nose. We're also leveraging thermal devices to take someone's temperature before they enter a congested area. And we're also using video analytics to see how much usage is happening in a specific room in an office building or even a hospital so we can determine if that room needs to be sanitized. So this is an example where, we had a really interesting worker safety use case but we're having to iterate quickly due to real world demands. And some of these demands were hard to predict and so we have to be able to react quickly. And again, using those cloud native best practices but applying them to the edge. Thank you so much for listening. If you'd like to learn more about Kubernetes everywhere go to ibm.biz slash kubcon 2020. Thank you so much and enjoy the rest of kubcon.